Markov Processes
An Introduction for Physical Scientists
- 1 Edición - 2 de diciembre de 1991
- Última edición
- Autor: Daniel T. Gillespie
- Idioma: Inglés
Markov process theory is basically an extension of ordinary calculus to accommodate functions whos time evolutions are not entirely deterministic. It is a subject that is becoming… Leer más
Descripción
Descripción
Markov process theory is basically an extension of ordinary calculus to accommodate functions whos time evolutions are not entirely deterministic. It is a subject that is becoming increasingly important for many fields of science. This book develops the single-variable theory of both continuous and jump Markov processes in a way that should appeal especially to physicists and chemists at the senior and graduate level.
Puntos claves
Puntos claves
- A self-contained, prgamatic exposition of the needed elements of random variable theory
- Logically integrated derviations of the Chapman-Kolmogorov equation, the Kramers-Moyal equations, the Fokker-Planck equations, the Langevin equation, the master equations, and the moment equations
- Detailed exposition of Monte Carlo simulation methods, with plots of many numerical examples
- Clear treatments of first passages, first exits, and stable state fluctuations and transitions
- Carefully drawn applications to Brownian motion, molecular diffusion, and chemical kinetics
De interès para
De interès para
Professionals/scientists without training in probability and statistics (using books as a "self-help" guide), senior undergraduate and graduate level students in physics and chemistry and mathematicians specializing in game theory, and finite math
Índice
Índice
Random Variable Theory. General Features of a Markov Process. Continuous Markov Processes. Jump Markov Processes with Continuum States. Jump Markov Processes with Discrete States. Temporally Homogeneous Birth-Death Markov Processes. Appendixes: Some Useful Integral Identities. Integral Representations of the Delta Functions. An Approximate Solution Procedure for "Open" Moment Evolution Equations. Estimating the Width and Area of a Function Peak. Can the Accuracy of the Continuous Process Simulation Formula Be Improved? Proof of the Birth-Death Stability Theorem. Solution of the Matrix Differential Equation. Bibliography. Index.
Detalles del producto
Detalles del producto
- Edición: 1
- Última edición
- Publicado: 31 de octubre de 2012
- Idioma: Inglés
Sobre el autor
Sobre el autor
DG
Daniel T. Gillespie
Afiliaciones y experiencia
Naval Weapons CenterVer libro en ScienceDirect
Ver libro en ScienceDirect
Lee Markov Processes en ScienceDirect